Optimal Analog-to-digital Converters Research Articles

Optimal Per-Antenna ADC Bit Allocation in Correlated and Cell-Free Massive MIMO

In Massive MIMO base stations (BSs), the hardware design needs to balance high spectral efficiency (SE) with low complexity. The level of hardware impairments (HWIs) indicates how strong the signal distortion introduced by hardware imperfections is. In particular, the analog-to-digital converters (ADCs) have an important impact on signal distortion and power consumption. This article addresses the fundamental problem of selecting the optimal hardware quality in the Massive MIMO space. In particular, we examine the optimal HWI and ADC bit allocation per BS antenna to maximize the SE. The results show that in co-located arrays with low channel gain variations across antennas, equal ADC bit allocation is optimal. In contrast, cell-free Massive MIMO systems benefit the most from optimizing the ADC bit allocation achieving improvements in the order of 2 [bit-per-channel-use] per user equipment when using regularized zero-forcing (RZF). In addition, when including the impact of power consumption in cell-free Massive MIMO with RZF, allocating low values of mixed ADC bit resolutions across the BS antennas can increase the energy efficiency up to 30% compared to equal ADC bit allocation.

Dynamic ADC Resolution-Mapping for Millimeter-Wave Massive MIMO Systems

The radio-frequency (RF) chains, phase shifters (PSs), and analog-to-digital converters (ADCs) play the dominant role of power consumption in the uplink hybrid millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) networks. To mitigate power consumption, an energy-efficient switch and inverter (SI) based partially-connected (PC) architecture with the Gram-Schmidt (GS) antenna selection strategy is addressed. The design of a variable-resolution ADC configuration is addressed under an independent upper bound of power consumption for each ADC in this paper. However, the resulting ADC resolution mapping becomes more complicated due to variant ADC power bounds. A simple ADC bit-allocation algorithm, namely, the sum-resolution (SR) ADC, is proposed. By replacing the total power constraint on ADCs to improve both the achievable sum-rate (ASR) and energy efficiency (EE) performance of the hybrid mmWave massive MIMO system. The SR-ADC solutions in closed form reveal that the optimal ADC resolution is proportional to the square power of the signal-to-noise ratio (SNR) in RF chains. Simulation results demonstrate that the proposed SR-ADC approach offers enhanced improvements on the ASR and EE, and exhibits prominent advantages on the number of activated RF chains compared with the fixed total power system.

A Radiation-Hardened SAR ADC with Delay-Based Dual Feedback Flip-Flops for Sensor Readout Systems.

For stable and effective control of the sensor system, analog sensor signals such as temperature, pressure, and electromagnetic fields should be accurately measured and converted to digital bits. However, radiation environments, such as space, flight, nuclear power plants, and nuclear fusion reactors, as well as high-reliability applications, such as automotive semiconductor systems, suffer from radiation effects that degrade the performance of the sensor readout system including analog-to-digital converters (ADCs) and cause system malfunctions. This paper investigates an optimal ADC structure in radiation environments and proposes a successive- approximation-register (SAR) ADC using delay-based double feedback flip-flops to enhance the system tolerance against radiation effects, including total ionizing dose (TID) and single event effects (SEE). The proposed flip-flop was fabricated using 130 nm complementary metal–oxide–semiconductor (CMOS) silicon-on-insulator (SOI) process, and its radiation tolerance was measured in actual radiation test facilities. Also, the proposed radiation-hardened SAR ADC with delay-based dual feedback flip-flops was designed and verified by utilizing compact transistor models, which reflect radiation effects to CMOS parameters, and radiation simulator computer aided design (CAD) tools.

Hybrid and full-digital beamforming in mmWave Massive MIMO systems: A comparison considering low-resolution ADCs

In a millimeter-wave (mmWave) Massive multiple-input multiple-output (MIMO) systems, full-digital beamforming (i.e., connecting each antenna with a specific radio-frequency (RF) chain) becomes inefficient due to the hardware cost and power consumption. Therefore, hybrid analog and digital transceiver where the number of RF chains are much smaller than that of the antennas has drawn great research interest. In this work, we investigate the use of low-resolution analog-to-digital converters (ADCs) in the uplink of multi-user hybrid and full-digital mmWave Massive MIMO systems. To be specific, we compare the performance of full-digital minimum mean square error (MMSE) and hybrid MMSE beamforming in both sum rates and energy efficiency. Accurate approximations of sum rates and energy efficiency are provided for both schemes, which captures the dominant factors. The analytical results show that full-digital beamforming outperforms hybrid beamforming in terms of sum rates and requires only a small portion (γ) of antennas used by hybrid beamforming to achieve the same sum rates. We given sufficient condition for full-digital beamforming to outperform hybrid beamforming in terms of energy efficiency. Moreover, an algorithm is proposed to search for the optimal ADC resolution bits. Numerical results demonstrate the correctness of the analysis.

Low-Cost Massive MIMO: Pilot Length and ADC Resolution

When designing an energy efficient massive multiple-input multiple-output (MIMO) system where each receiver antenna is equipped with a low-resolution analog-to-digital converter (ADC), the number of base station (BS) antennas and quantization bits are generally two mutually conflicting system parameters. In this paper, we investigate the joint optimization of the number of BS antennas and ADC resolution in quantized massive MIMO systems, assuming imperfect channel state information (CSI). A tractable approximate expression for the uplink sum spectral efficiency (SE) using maximal ratio combining (MRC) receivers is derived, based on which the pilot length which maximizes the sum SE is put forward. Considering the effect of ADCs, a realistic model of total power consumption is given subsequently. Capitalizing on it, we formulate the optimization problem of selecting the number of BS antennas and ADC resolution to maximize the sum SE under a total power consumption constraint. Our results show that more pilot symbols should be assigned for massive MIMO systems with low-resolution ADCs, especially for the receivers with one-bit quantizers. Moreover, the results show the trade-off between the number of BS antennas and quantization bits. Numerical results suggest that there exists an optimal ADC resolution in massive MIMO systems, while lower quantization bits may cause a substantial degradation of the SE performance and higher one will consume more power.

Multi-pair massive MIMO amplify-and-forward relaying system with low-resolution ADCs: performance analysis and power control

In this paper, we focus on a general multi-pair massive MIMO amplify-and-forward (AF) relaying system where the relay antennas employ low-resolution analog-to-digital converters (ADCs) to reduce the hardware cost. First, considering the effect of low quantization on channel estimation, a tight closed form approximation of the system ergodic achievable rate is derived. Second, some asymptotic analysis is presented to reveal the impacts of the system parameters on the achievable rate. Particularly, the generalized power scaling schemes are characterized. The results indicate that in some cases, when the number of relay antennas grows without bound, the impact of the finite resolution ADCs on data transmission can be eliminated. To enhance the achievable rate of the quantized systems, the optimal user and relay power control schemes are proposed. Furthermore, to reap all the benefits of low-resolution ADCs, another power control scheme is also designed to minimize the total power consumption while guaranteeing the quality-of-service (QoS) requirement of each user, which can help draw some useful insights into the optimal ADC resolution from power saving perspectives. The simulation results confirm the accuracy of our theoretical analysis and the effectiveness of the proposed power control schemes.

When Are Low Resolution ADCs Energy Efficient in Massive MIMO?

Massive MIMO (MaMI) is often promoted as a technology that will enable the use of low-quality, cheap hardware. One particular component that has been in the focus of MaMI-related research is the analog-to-digital converter (ADC), and use of very low-resolution ADCs has been proposed. However, studies about whether this strategy is justified from an energy-efficiency point of view have largely been inconclusive. In this paper, we choose system setup and models that reflect the hardware implementation reality as close as possible and perform a parametric analysis of uplink energy efficiency as a function of ADC resolution. If antenna scaling and decrease of ADC resolution are considered independently, the energy efficiency is shown to be maximized at intermediate ADC resolutions, typically in the range of 4–8 bits. Moreover, optimal ADC resolution does not decrease when more antennas are used except in some specific cases, and when it does, the decrease is approximately logarithmic in the number of antennas. In the case when antenna scaling and ADC degradation are coupled through a constant-performance constraint, it is shown that energy efficiency cannot improve with reduced bit resolution unless the power consumption of blocks other than ADCs scales down with the upscaling of antennas at a fast enough rate. Altogether it is concluded that in MaMI, intermediate ADC resolutions are optimal in energy efficiency sense, and, except in some special cases, scaling up the antennas to very large numbers does not change this conclusion.

Performance of ADC resolution-limited optical coherent receivers with DA-ML carrier phase estimation

We investigate the performance of decision-aided maximum likelihood (DA-ML) carrier phase estimation (CPE) algorithm in analog-to-digital converter (ADC) resolution-limited optical coherent receivers. The signal model of ADC resolution-limited receiver is established, and its bit error rate with DA-ML CPE in M-ary phase-shift keying (MPSK) system is analyzed. Simulation results show that $$N+2$$N+2 (N bits per symbol)-bit resolution is acceptable and $$N+3$$N+3 bit is sufficient for DA-ML receiver in MPSK $$(M=4, 8, 16)$$(M=4,8,16) system, and for 16-ary quadrature amplitude modulation system, 6-bit resolution is reasonable. Simulation and theoretical analyses both indicate that the optimal ADC quantization range equals to the average amplitude of received symbols which carry the information symbols with the highest energy on the constellation.

BER-Optimal Analog-to-Digital Converters for Communication Links

In this paper, we propose low precision BER-optimal analog-to-digital converters (ADC) where quantization levels and thresholds are set nonuniformly to minimize the bit-error rate (BER ) in a high data rate communication link. This is in contrast to how ADCs are used today, in which they act as transparent waveform preservers. Simulations for various communication channels show that for 2-PAM modulation, the BER-optimal ADC achieves shaping gains that range from 2.5 dB for channels with low intersymbol interference (ISI) to more than 30 dB for channels with high levels of ISI. Moreover, a 3-bit BER -optimal ADC achieves the same or even lower BER than a 4-bit uniform ADC. With a DFE and a high-ISI channel, a 3-bit BER -optimal ADC achieves lower BER than a 5-bit uniform ADC.